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# mypy: allow-untyped-defs
import torch
import torch.ao.nn.intrinsic as nni
import torch.ao.nn.quantized as nnq
from torch.ao.nn.quantized.modules.utils import _quantize_weight
__all__ = [
"LinearReLU",
"LinearLeakyReLU",
"LinearTanh",
]
class LinearReLU(nnq.Linear):
r"""
A LinearReLU module fused from Linear and ReLU modules
We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
Attributes:
Same as torch.ao.nn.quantized.Linear
Examples::
>>> # xdoctest: +SKIP
>>> m = nn.intrinsic.LinearReLU(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nni.LinearReLU # type: ignore[assignment]
def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
super().__init__(in_features, out_features, bias, dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.linear_relu(
x, self._packed_params._packed_params, self.scale, self.zero_point
)
def _get_name(self):
return "QuantizedLinearReLU"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
return super().from_float(mod, use_precomputed_fake_quant)
@classmethod
def from_reference(cls, ref_linear_relu, output_scale, output_zero_point):
return super().from_reference(
ref_linear_relu[0], output_scale, output_zero_point
)
class LinearLeakyReLU(nnq.Linear):
r"""
For onednn backend only
A LinearLeakyReLU module fused from Linear and LeakyReLU modules
We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
Attributes:
Same as torch.ao.nn.quantized.Linear
+ negative_slope
Examples::
>>> # xdoctest: +SKIP
>>> m = nn.intrinsic.LinearLeakyReLU(20, 30, 0.01)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nni.LinearLeakyReLU # type: ignore[assignment]
def __init__(
self, in_features, out_features, negative_slope, bias=True, dtype=torch.qint8
):
super().__init__(in_features, out_features, bias, dtype)
self.negative_slope = negative_slope
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.linear_leaky_relu(
x,
self._packed_params._packed_params,
self.scale,
self.zero_point,
self.negative_slope,
)
def _get_name(self):
return "QuantizedLinearLeakyReLU"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
assert (
type(mod) == nni.LinearLeakyReLU
), "Input float module should be LinearLeakyReLU"
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
activation_post_process = mod.activation_post_process
leaky_relu = mod[1]
mod = mod[0]
weight_post_process = mod.qconfig.weight()
weight_post_process(mod.weight)
dtype = weight_post_process.dtype
act_scale, act_zp = activation_post_process.calculate_qparams() # type: ignore[union-attr,operator]
assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
qweight = _quantize_weight(mod.weight.float(), weight_post_process)
qlinear_leaky_relu = cls(
mod.in_features, mod.out_features, leaky_relu.negative_slope, dtype=dtype
)
qlinear_leaky_relu.set_weight_bias(qweight, mod.bias)
qlinear_leaky_relu.scale = float(act_scale)
qlinear_leaky_relu.zero_point = int(act_zp)
return qlinear_leaky_relu
@classmethod
def from_reference(cls, ref_mod, output_scale, output_zero_point):
linear = ref_mod[0]
leaky_relu = ref_mod[1]
qlinear_leaky_relu = cls(
linear.in_features, linear.out_features, leaky_relu.negative_slope
)
qweight = linear.get_quantized_weight()
qlinear_leaky_relu.set_weight_bias(qweight, linear.bias)
qlinear_leaky_relu.scale = float(output_scale)
qlinear_leaky_relu.zero_point = int(output_zero_point)
return qlinear_leaky_relu
class LinearTanh(nnq.Linear):
r"""
A LinearTanh module fused from Linear and Tanh modules
We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
Attributes:
Same as torch.ao.nn.quantized.Linear
Examples::
>>> # xdoctest: +SKIP
>>> m = nn.intrinsic.LinearTanh(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
_FLOAT_MODULE = nni.LinearTanh # type: ignore[assignment]
def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
super().__init__(in_features, out_features, bias, dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.quantized.linear_tanh(
x, self._packed_params._packed_params, self.scale, self.zero_point
)
def _get_name(self):
return "QuantizedLinearTanh"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
assert type(mod) == nni.LinearTanh, "Input float module should be LinearTanh"
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
activation_post_process = mod.activation_post_process
mod = mod[0]
weight_post_process = mod.qconfig.weight()
weight_post_process(mod.weight)
dtype = weight_post_process.dtype
act_scale, act_zp = activation_post_process.calculate_qparams() # type: ignore[union-attr,operator]
assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
qweight = _quantize_weight(mod.weight.float(), weight_post_process)
qlinear_tanh = cls(mod.in_features, mod.out_features, dtype=dtype)
qlinear_tanh.set_weight_bias(qweight, mod.bias)
qlinear_tanh.scale = float(act_scale)
qlinear_tanh.zero_point = int(act_zp)
return qlinear_tanh
@classmethod
def from_reference(cls, ref_mod, output_scale, output_zero_point):
linear = ref_mod[0]
qlinear_tanh = cls(linear.in_features, linear.out_features)
qweight = linear.get_quantized_weight()
qlinear_tanh.set_weight_bias(qweight, linear.bias)
qlinear_tanh.scale = float(output_scale)
qlinear_tanh.zero_point = int(output_zero_point)
return qlinear_tanh
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